As Molly-Mae infamously stated, “we all have the same 24 hours in a day”, but how does time expenditure differ across the globe? (Hague & Bartlett, 2022).
With my background as a medical student, I’m aware of how lifestyle-related health conditions vary in prevalence across the world. For example, 60% of Type 2 diabetics are of Asian heritage. (Malik, Willett & Hu, 2012).
A more detailed understanding of global time-use variance could therefore highlight key lifestyle differences which may be contributing to health inequalities.
This project aimed to gain further insight into time-use variability across across 6 chosen countries: USA, UK, Spain, Poland, China and Korea, casting a specific focus on comparison between European and East Asian time expenditure.
Thus, the following research questions were outlined:
Which time-use category is the highest for each country? Which is the lowest?
Are there any time-use categories that are significantly different between European and East Asian countries?
Please visit my github repository to access all documentation related to this project!
The data used in this project was sourced from ‘Our World In Data’ (https://ourworldindata.org/time-use), and was originally collected by The Organisation for Economic Co-operation and Development (OECD), an inter-governmental economic organisation.
Participants completed time-use diaries recording their sequence of activity during 24 hours, alongside specific questionnaires where respondents recalled the proportion of time spent in a specific activity category (information on the questions asked are not detailed in the original data).
The responses from people aged 15-64 were collected from 33 countries, averaged, and sorted into 14 separate activity categories. (Ortiz-Ospina, Giattino & Roser, 2022).
More detailed explanations of the variables used and data origins can be accessed in the codebook here:
#loading the time use data and assigning to variable df
df <- read.csv(here("data", "time_use_data.csv"), fileEncoding = 'UTF-8-BOM')#re-naming dataframe columns
df_renamed <- df %>%
rename(country = Country, category = Category,
time = Time..minutes.)#creating data frames of the countries
#we want to visualize:
#korea
df_korea <- df_renamed %>%
filter(country == "Korea")
#UK
df_uk <- df_renamed %>%
filter(country == "UK")
#China
df_china <- df_renamed %>%
filter(country == "China")
#Poland
df_poland <- df_renamed %>%
filter(country == "Poland")
#USA
df_usa <- df_renamed %>%
filter(country == "USA")
#Spain
df_spain <- df_renamed %>%
filter(country == "Spain")I combined the following activities to reduce the number of categories to 9, improving clarity of the visualization:
paid work + unpaid work –> work and volunteering
housework + care for household members –> household tasks
TV&radio + attending events + seeing friends + other leisure –> other leisure
#Combining rows to reduce number of categories
#for each country:
#combining rows Korea
df_korea[15,3] <- df_korea[5,3]+ df_korea[11,3] +
df_korea[13,3] + df_korea[14,3]
df_korea[16,3] <- df_korea[3,3] + df_korea[4,3]
df_korea[17,3] <- df_korea[1,3] + df_korea[6,3]
#combining rows uk
df_uk[15,3] <- df_uk[5,3]+ df_uk[11,3] +
df_uk[13,3] + df_uk[14,3]
df_uk[16,3] <- df_uk[3,3] + df_uk[4,3]
df_uk[17,3] <- df_uk[1,3] + df_uk[6,3]
#combining rows China
df_china[15,3] <- df_china[5,3]+ df_china[11,3] +
df_china[13,3] + df_china[14,3]
df_china[16,3] <- df_china[3,3] + df_china[4,3]
df_china[17,3] <- df_china[1,3] + df_china[6,3]
#combining rows Poland
df_poland[15,3] <- df_poland[5,3]+ df_poland[11,3] + df_poland[13,3] + df_poland[14,3]
df_poland[16,3] <- df_poland[3,3] + df_poland[4,3]
df_poland[17,3] <- df_poland[1,3] + df_poland[6,3]
#combining rows USA
df_usa[15,3] <- df_usa[5,3]+ df_usa[11,3] +
df_usa[13,3] + df_usa[14,3]
df_usa[16,3] <- df_usa[3,3] + df_usa[4,3]
df_usa[17,3] <- df_usa[1,3] + df_usa[6,3]
#combining rows Spain
df_spain[15,3] <- df_spain[5,3]+ df_spain[11,3] +
df_spain[13,3] + df_spain[14,3]
df_spain[16,3] <- df_spain[3,3] + df_spain[4,3]
df_spain[17,3] <- df_spain[1,3] + df_spain[6,3]#assigning new category names to the combined rows
#for each country:
#new categories Korea
df_korea[15,2] <- "Other leisure"
df_korea[15,1] <- "Korea"
df_korea[16,2] <- "Household tasks"
df_korea[16,1] <- "Korea"
df_korea[17,2] <- "Work and Volunteering"
df_korea[17,1] <- "Korea"
#new categories UK
df_uk[15,2] <- "Other leisure"
df_uk[15,1] <- "UK"
df_uk[16,2] <- "Household tasks"
df_uk[16,1] <- "UK"
df_uk[17,2] <- "Work and Volunteering"
df_uk[17,1] <- "UK"
#new categories China
df_china[15,2] <- "Other leisure"
df_china[15,1] <- "China"
df_china[16,2] <- "Household tasks"
df_china[16,1] <- "China"
df_china[17,2] <- "Work and Volunteering"
df_china[17,1] <- "China"
#new categories Poland
df_poland[15,2] <- "Other leisure"
df_poland[15,1] <- "Poland"
df_poland[16,2] <- "Household tasks"
df_poland[16,1] <- "Poland"
df_poland[17,2] <- "Work and Volunteering"
df_poland[17,1] <- "Poland"
#new categories USA
df_usa[15,2] <- "Other leisure"
df_usa[15,1] <- "USA"
df_usa[16,2] <- "Household tasks"
df_usa[16,1] <- "USA"
df_usa[17,2] <- "Work and Volunteering"
df_usa[17,1] <- "USA"
#new categories Spain
df_spain[15,2] <- "Other leisure"
df_spain[15,1] <- "Spain"
df_spain[16,2] <- "Household tasks"
df_spain[16,1] <- "Spain"
df_spain[17,2] <- "Work and Volunteering"
df_spain[17,1] <- "Spain"#CREATING FUNCTIONS
#FUNCTION_001 --> deleting unnecessary rows
function_001 <- function(df){subset(df, category!="Shopping" &
category!="Attending events" &
category!="TV and Radio" &
category!="Other leisure activities" &
category!="Housework" &
category!="Other unpaid work & volunteering" &
category!="Paid work" &
category!="Care for household members ")}
#applying to each country data frame
df_uk <- function_001(df_uk)
df_korea <- function_001(df_korea)
df_china <- function_001(df_china)
df_poland <- function_001(df_poland)
df_usa <- function_001(df_usa)
df_spain <- function_001(df_spain)#FUNCTION_002 --> creating a duration column which contains time spent in each category
#in seconds, hours and minutes
function_002 <- function(df){
mutate(df, duration = dminutes(time))
}
#applying to each country data frame
df_uk <- function_002(df_uk)
df_korea <- function_002(df_korea)
df_china <- function_002(df_china)
df_poland <- function_002(df_poland)
df_usa <- function_002(df_usa)
df_spain <- function_002(df_spain)#FUNCTION_003 --> creating an hours column containing
#time spent in each category as a decimal of an hour
function_003 <- function(df){
mutate(df, hours = df$time/60)
}
#applying to each country data frame
df_uk <- function_003(df_uk)
df_korea <- function_003(df_korea)
df_china <- function_003(df_china)
df_poland <- function_003(df_poland)
df_usa <- function_003(df_usa)
df_spain <- function_003(df_spain)#FUNCTION_004 --> deleting seconds from the duration column
function_004 <- function(df){
df%>%
separate(duration, into = c(NA, "duration"), sep = "~",
convert = TRUE, remove = FALSE, fill = "right")
}
#applying to each country data frame
df_uk <- function_004(df_uk)
df_korea <- function_004(df_korea)
df_china <- function_004(df_china)
df_poland <- function_004(df_poland)
df_usa <- function_004(df_usa)
df_spain <- function_004(df_spain)#FUNCTION_005 --> deleting unnecessary
#bracket from duration column
function_005 <- function(df){
gsub("[)|]", "",
df$duration)}
#applying to each country data frame duration column
df_uk$duration <- function_005(df_uk)
df_korea$duration <- function_005(df_korea)
df_china$duration <- function_005(df_china)
df_poland$duration <- function_005(df_poland)
df_usa$duration <- function_005(df_usa)
df_spain$duration <- function_005(df_spain)#FUNCTION_006 --> filtering out durations
#containing "minutes"
function_006 <- function(df){
df[grep("minutes", df$duration), ]
}
#apply to each country data frame and save in a new
#data frame in the form country_mins
uk_mins <- function_006(df_uk)
korea_mins <- function_006(df_korea)
china_mins <- function_006(df_china)
poland_mins <- function_006(df_poland)
usa_mins <- function_006(df_usa)
spain_mins <- function_006(df_spain)#FUNCTION_007 --> filtering out durations
#containing "hours"
function_007 <- function(df){
df[grep("hours", df$duration), ]
}
#apply to each country data frame and save in a new
#data frame in the form country_hours
uk_hours <- function_007(df_uk)
korea_hours <- function_007(df_korea)
china_hours <- function_007(df_china)
poland_hours <- function_007(df_poland)
usa_hours <- function_007(df_usa)
spain_hours <- function_007(df_spain)#FUNCTION_008 --> deleting the word "hours" from duration column
function_008 <- function(df){
gsub("[hours|]", "", df$duration)
}
#apply to each country_hours data frame
uk_hours$duration <- function_008(uk_hours)
korea_hours$duration <- function_008(korea_hours)
china_hours$duration <- function_008(china_hours)
poland_hours$duration <- function_008(poland_hours)
usa_hours$duration <- function_008(usa_hours)
spain_hours$duration <- function_008(spain_hours)#FUNCTION_009 --> converting duration column to numerical data
function_009 <- function(df){
as.numeric(df$duration)
}
#applying to each country_hours duration column
uk_hours$duration <- function_009(uk_hours)
korea_hours$duration <- function_009(korea_hours)
china_hours$duration <- function_009(china_hours)
poland_hours$duration <- function_009(poland_hours)
usa_hours$duration <- function_009(usa_hours)
spain_hours$duration <- function_009(spain_hours)#FUNCTION_010 --> calculating whole hours
#and residual minutes
function_010 <- function(df){
{hrs <- floor(df$duration)}
{mins <- round(df$duration %% hrs * 60, 0)}
{paste0(hrs, "h ", mins, "m")}
}
#applying to each country_hours duration column
uk_hours$duration <- function_010(uk_hours)
korea_hours$duration <- function_010(korea_hours)
china_hours$duration <- function_010(china_hours)
poland_hours$duration <- function_010(poland_hours)
usa_hours$duration <- function_010(usa_hours)
spain_hours$duration <- function_010(spain_hours)#FUNCTION_011 --> combining the country_hours
#and country_mins data frames
function_011 <- function(df1, df2){
rbind(df1, df2)
}
#applying to each country data frame
df_uk <- function_011(uk_hours, uk_mins)
df_korea <- function_011(korea_hours, korea_mins)
df_china <- function_011(china_hours, china_mins)
df_poland <- function_011(poland_hours, poland_mins)
df_usa<- function_011(usa_hours, usa_mins)
df_spain <- function_011(spain_hours, spain_mins)#FUNCTION_012 --> shortening "minutes" to "m" in duration column
function_012 <- function(df){
str_replace(df$duration, " minutes", "m")
}
#applying to each country duration column
df_uk$duration <- function_012(df_uk)
df_korea$duration <- function_012(df_korea)
df_china$duration <- function_012(df_china)
df_poland$duration <- function_012(df_poland)
df_usa$duration <- function_012(df_usa)
df_spain$duration <- function_012(df_spain)#FUNCTION_013 --> converting time column to numeric function
function_013 <- function(df){
as.numeric(df$time)
}
#applying to each country data frame
function_013(df_uk)## [1] 508 79 267 136 297 27 58 19 47function_013(df_korea)## [1] 471 117 90 201 104 330 57 27 42function_013(df_china)## [1] 542 100 202 126 348 25 52 23 23function_013(df_poland)## [1] 509 91 243 173 268 30 57 23 45function_013(df_usa)## [1] 528 63 251 131 316 31 57 18 44function_013(df_spain)## [1] 516 126 248 151 230 26 51 42 51#FUNCTION_014 --> sorting by alphabetical category
function_014 <- function(df){
df[order(df$category),]
}
#apply to each country data frame
df_uk <- function_014(df_uk)
df_korea <- function_014(df_korea)
df_china <- function_014(df_china)
df_poland <- function_014(df_poland)
df_usa <- function_014(df_usa)
df_spain <- function_014(df_spain)#FUNCTION_015 --> defining label position in the middle
#of each bar
function_015 <- function(df){
(cumsum(df$hours) - df$hours/2)
}
#apply to each country data frame, creating new column "cumulative"
df_uk$cumulative <- function_015(df_uk)
df_korea$cumulative <- function_015(df_korea)
df_china$cumulative <- function_015(df_china)
df_poland$cumulative <- function_015(df_poland)
df_usa$cumulative <- function_015(df_usa)
df_spain$cumulative <- function_015(df_spain)#FUNCTION_016 --> calculating percentage of time
#spent in each category
function_016 <- function(df){
result <- (df / 24) * 100
round <- round(result, digits = 1)
return(round)
}
#apply to each country data frame and create
#new column "percent"
df_uk$percent <- function_016(df_uk$hours)
df_korea$percent <- function_016(df_korea$hours)
df_china$percent <- function_016(df_china$hours)
df_poland$percent <- function_016(df_poland$hours)
df_usa$percent <- function_016(df_usa$hours)
df_spain$percent <- function_016(df_spain$hours)#combining all complete dataframes
df_all <- rbind(df_uk, df_korea, df_china,
df_poland, df_usa, df_spain)#defining colour palette
palette <- colorRampPalette(c("tomato", "orange", "yellow", "green", "lightblue", "pink"))
#defining margins
m <- list(
l = 0,
r = 0,
b = 80,
t = 80,
pad = 0
)An interactive stacked bar plot was created using the plot_ly package, allowing clear visual comparison of the proportion of daily time spent in each activity category per country. The percentage of total time spent in each category is detailed in the hover function, providing a more in-depth analysis of the data.
#PLOTTING AN INTERACTIVE GRAPH WITH PLOTLY
#Loading complete data frame into plot_ly function, assigning x variable as hours and y variable as country, assigning each category a colour from palette
p <- df_all %>%
plot_ly(x=~hours, y=~country,
color=~category, colors = palette(9),
#Plot type bar, adjusting height and width, adding a black line between each bar and assigning percent to the hover information
type = "bar", width=1400, height= 400,
marker = list(line = list(color = "rgba(0, 0, 0, 0.5)", width = 1.5)),
hovertemplate = paste(df_all$percent,"%"))%>%
#Adjusting layout with title, subtitle, x-axis title, removing zeroline, and increasing number of tickmarks
layout(title = "Daily time use by country <br><sup> Averaged from time-use diaries from people aged 15-64 </sup>",
xaxis = list(title = "Duration (hours)", zeroline = FALSE, nticks=24),
#removing yaxis title and zeroline, and creating a stacked bar chart
yaxis = list(title = "", zeroline = FALSE), barmode = "stack",
#adjusting legend font size and applying margin parameters
showlegend = TRUE, legend = list(font = list(size = 10)), margin = m) %>%
#adding text labels in bold to the middle of each bar category using cumulative column positions, adjusting font size and removing arrow
add_annotations(text = sprintf("<b>%s</b>", df_all$duration), x = c(df_all$cumulative),
y=~country, showarrow = FALSE, font = list(size = 5)) %>%
config(displayModeBar = FALSE)#saving the plot
library(htmlwidgets)
saveWidget(p, here("plots", "time_use_plot.html"))#view plot
pThis project gained an interesting insight into how cultural and geographical differences impact daily time expenditure.
The highest proportion of time spent across the board was in sleep, followed by work/volunteering and leisure.
Whilst each country has minor differences in time use, it is evident that European countries spend a much smaller proportion of their time working and volunteering, and more on leisure when compared with the East Asian countries in this study.
Understanding how different cultures spend each day is not only important for forming inclusive relationships, but could also provide insight into lifestyle differences that may contribute to health issues across the world.
However, as such a large age range is included in this data set, specific age-related lifestyle differences cannot be assessed.
Thus, moving forward, further time-use data should be collected for a wider range of countries, and separated into smaller age categories to deepen understanding of cultural lifestyle differences and how this changes throughout a lifetime.
This could be plotted alongside prevelence of lifestyle-related health issues, such as Type 2 diabetes, for each country to investigate whether cultural lifestyle differences significantly impact health outcomes.
Ortiz-Ospina, E., Giattino, C., & Roser, M. (2022). Time Use. Retrieved 25 April 2022, from https://ourworldindata.org/time-use
Hague, M., & Bartlett, S. (2022). The Diary Of A CEO with Steven Bartlett: E110: Molly Mae: How She Became Creative Director Of PLT At 22 on Apple Podcasts. Retrieved 25 April 2022, from https://podcasts.apple.com/gb/podcast/e110-molly-mae-how-she-became-creative-director-of-plt-at-22/id1291423644?i=1000544772150
Malik, V., Willett, W., & Hu, F. (2012). Global obesity: trends, risk factors and policy implications. Nature Reviews Endocrinology, 9(1), 13-27. doi: 10.1038/nrendo.2012.199